deepsort源码解读(六)

deepsort项目地址

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目录
deepsort原理及解析汇总

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track.py

# vim: expandtab:ts=4:sw=4


class TrackState:
    """
    Enumeration type for the single target track state. Newly created tracks are
    classified as `tentative` until enough evidence has been collected. Then,
    the track state is changed to `confirmed`. Tracks that are no longer alive
    are classified as `deleted` to mark them for removal from the set of active
    tracks.

    """
    # tentative即所谓的unconfirmed
    Tentative = 1
    Confirmed = 2
    Deleted = 3


class Track:
    """
    A single target track with state space `(x, y, a, h)` and associated
    velocities, where `(x, y)` is the center of the bounding box, `a` is the
    aspect ratio and `h` is the height.

    Parameters
    ----------
    mean : ndarray
        Mean vector of the initial state distribution.
    covariance : ndarray
        Covariance matrix of the initial state distribution.
    track_id : int
        A unique track identifier.
    n_init : int
        Number of consecutive detections before the track is confirmed. The
        track state is set to `Deleted` if a miss occurs within the first
        `n_init` frames.
    max_age : int
        The maximum number of consecutive misses before the track state is
        set to `Deleted`.
    feature : Optional[ndarray]
        Feature vector of the detection this track originates from. If not None,
        this feature is added to the `features` cache.

    Attributes
    ----------
    mean : ndarray
        Mean vector of the initial state distribution.
    covariance : ndarray
        Covariance matrix of the initial state distribution.
    track_id : int
        A unique track identifier.
    hits : int
        Total number of measurement updates.
        表示匹配上了多少次，匹配次数超过n_init就会设置为confirmed状态
    age : int
        Total number of frames since first occurance.
    time_since_update : int
        Total number of frames since last measurement update.
        表示失配的次数；被匹配时会被置零
    state : TrackState
        The current track state.
    features : List[ndarray]
        A cache of features. On each measurement update, the associated feature
        vector is added to this list.
        每个track对应多个features,每次更新都将最新的feature添加到列表中.
        意义在于解决目标被遮挡后再次出现的问题，需要根据以往帧对应的特征inx匹配
    """

    def __init__(self, mean, covariance, track_id, n_init, max_age,
                 feature=None):
        self.mean = mean
        self.covariance = covariance
        self.track_id = track_id
        self.hits = 1
        self.age = 1
        self.time_since_update = 0

        self.state = TrackState.Tentative
        self.features = []
        if feature is not None:
            self.features.append(feature)

        self._n_init = n_init
        self._max_age = max_age

    def to_tlwh(self):
        """Get current position in bounding box format `(top left x, top left y,
        width, height)`.

        Returns
        -------
        ndarray
            The bounding box.

        """
        ret = self.mean[:4].copy()
        ret[2] *= ret[3]
        ret[:2] -= ret[2:] / 2
        return ret

    def to_tlbr(self):
        """Get current position in bounding box format `(min x, miny, max x,
        max y)`.

        Returns
        -------
        ndarray
            The bounding box.

        """
        ret = self.to_tlwh()
        ret[2:] = ret[:2] + ret[2:]
        return ret

    # 利用卡尔曼滤波预测前一帧中的tracks在当前帧的状态
    def predict(self, kf):
        """Propagate the state distribution to the current time step using a
        Kalman filter prediction step.

        Parameters
        ----------
        kf : kalman_filter.KalmanFilter
            The Kalman filter.

        """
        # mean和covariance的功能
        # 根据卡尔曼去预测这些track在当前帧的位置mean与covariance.
        # 然后根据预测的均值和方差进行track与detection的匹配
        # 卡尔曼预测的均值和方差的作用，在gate_cost_matrix()参与实现
        # mean的格式[中心点坐标,w/h,h]
        self.mean, self.covariance = kf.predict(self.mean, self.covariance)
        self.age += 1
        self.time_since_update += 1

    # 匹配成功，进行更新参数值
    def update(self, kf, detection):
        """Perform Kalman filter measurement update step and update the feature
        cache.

        Parameters
        ----------
        kf : kalman_filter.KalmanFilter
            The Kalman filter.
        detection : Detection
            The associated detection.

        """
        self.mean, self.covariance = kf.update(
            self.mean, self.covariance, detection.to_xyah())
        self.features.append(detection.feature)

        self.hits += 1
        # 匹配成功，将time_since_update置为零
        self.time_since_update = 0
        if self.state == TrackState.Tentative and self.hits >= self._n_init:
            self.state = TrackState.Confirmed

    # 标记缺失，改为删除状态
    def mark_missed(self):
        """Mark this track as missed (no association at the current time step).
        """
        if self.state == TrackState.Tentative:
            self.state = TrackState.Deleted
        elif self.time_since_update > self._max_age:
            self.state = TrackState.Deleted

    # 状态判断
    def is_tentative(self):
        """Returns True if this track is tentative (unconfirmed).
        """
        return self.state == TrackState.Tentative

    def is_confirmed(self):
        """Returns True if this track is confirmed."""
        return self.state == TrackState.Confirmed

    def is_deleted(self):
        """Returns True if this track is dead and should be deleted."""
        return self.state == TrackState.Deleted